Mobility data has attracted the researchers for the past few years because of its rich context and spatiotemporal nature, where this information can be used for potential applications like early warning system, route prediction, traffic management, advertisement, social networking, and community finding. All the mentioned applications are based on mobility profile building and user trend analys...

This work presents an application of diverse soft-computing techniques to the resolution of semaphoric regulation problems. First, clustering techniques are used to discover the prototypes which characterize the mobility patterns at an intersection. A prediction model is then constructed on the basis of the prototypes found. Fuzzy logic techniques are used to formally represent the prototypes i...

In the wireless network, whenever a mobile node moves from one cell to another called handover or handoff, the call needs to be handed off to the new base station, and then network resources must be reallocated. Many mobility prediction schemes are proposed to perform resource reservations in advance so as to reduce the handover latency. Such approaches make use of knowledge patterns of locatio...

Outdoor robots such as planetary rovers must be able to navigate safely and reliably in order to successfully perform missions in remote or hostile environments. Mobility prediction is critical to achieving this goal due to the inherent control uncertainty faced by robots traversing natural terrain. We propose a novel algorithm for stochastic mobility prediction based on multi-output Gaussian p...

Identifying the patterns in urban mobility is important for a variety of tasks such as transportation planning, urban resource allocation, emergency planning etc. This is evident from the large body of research on the topic, which has exploded with the vast amount of geo-tagged user-generated content from online social media. However, most of the existing work focuses on a specific setting, tak...

We present and test a sequential learning algorithm for the prediction of human mobility that leverages large datasets of sequences to improve prediction accuracy, in particular for users with a short and non-repetitive data history such as tourists in a foreign country. The algorithm compensates for the difficulty of predicting the next location when there is limited evidence of past behavior ...

This paper presents an application of diverse soft-computing techniques to adaptive traffic light controls. The proposed methodology consists of two main phases: off-line and on-line. First, clustering techniques and optimization methods are used at the off-line stage to discover the prototypes which characterize the traffic mobility patterns at an intersection. After this process an optimum ti...

In mobile and ubiquitous computing the location of devices is often important both for the behavior of the applications and for communication and other middleware functionality. Mobility prediction enables proactively dealing with changes in location dependent functionality. In this project Bayesian networks’ ability to reason on the basis of incomplete or inaccurate information is powering mob...

This paper presents a source based reactive protocol called “Energy aware Multipath QoS Routing Protocol with Mobility Prediction (EMQRPMP)” for MANET. It is the enhanced version of the existing protocol called “Power aware Multiple QoS constraints Routing Protocol with Mobility Prediction (PMQRPMP)”. It considers quality of service constraints namely delay, delay-jitter, bandwidth, and cost fo...

INTRODUCTION: Prediction of emergency mobility needs to consider more scenarios as Internet Things (IoT) develops at a high speed, which influences the quality and quantity data, manageable resources algorithms. OBJECTIVES: This work investigates differences in dynamic prediction when facing temporal IoT data with different considering diverse computing algorithm availability. METHODS: A node c...